使用Pandas将CSV读取到具有不同行长的dataFrame中 [英] Read CSV into a dataFrame with varying row lengths using Pandas

问题描述

所以我有一个看起来像这样的CSV:

So I have a CSV that looks a bit like this:

1 | 01-01-2019 | 724
2 | 01-01-2019 | 233 | 436
3 | 01-01-2019 | 345
4 | 01-01-2019 | 803 | 933 | 943 | 923 | 954
5 | 01-01-2019 | 454
...

当我尝试使用以下代码生成dataFrame时.

And when I try to use the following code to generate a dataFrame..

df = pd.read_csv('data.csv', header=0, engine='c', error_bad_lines=False)

它仅将3列的行添加到df(上方的第1、3和5行)

It only adds rows with 3 columns to the df (rows 1, 3 and 5 from above)

其余的被认为是坏线"，给我以下错误:

The rest are considered 'bad lines' giving me the following error:

Skipping line 17467: expected 3 fields, saw 9

如何创建一个在csv中包含所有数据的数据框，可能只是将空单元格填充为null?还是在添加到df之前必须声明最大行长?

How do I create a data frame that includes all data in my csv, possibly just filling in the empty cells with null? Or do I have to declare the max row length prior to adding to the df?

谢谢！

推荐答案

如果仅使用pandas，请逐行读取，然后处理分隔符.

If using only pandas, read in lines, deal with the separator after.

import pandas as pd

df = pd.read_csv('data.csv', header=None, sep='\n')
df = df[0].str.split('\s\|\s', expand=True)

   0           1    2     3     4     5     6
0  1  01-01-2019  724  None  None  None  None
1  2  01-01-2019  233   436  None  None  None
2  3  01-01-2019  345  None  None  None  None
3  4  01-01-2019  803   933   943   923   954
4  5  01-01-2019  454  None  None  None  None

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